# Update GPT4ALL integration
GPT4ALL have completely changed their bindings. They use a bit odd
implementation that doesn't fit well into base.py and it will probably
be changed again, so it's a temporary solution.
Fixes#3839, #4628
# Docs: compound ecosystem and integrations
**Problem statement:** We have a big overlap between the
References/Integrations and Ecosystem/LongChain Ecosystem pages. It
confuses users. It creates a situation when new integration is added
only on one of these pages, which creates even more confusion.
- removed References/Integrations page (but move all its information
into the individual integration pages - in the next PR).
- renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations.
I like the Ecosystem term. It is more generic and semantically richer
than the Integration term. But it mentally overloads users. The
`integration` term is more concrete.
UPDATE: after discussion, the Ecosystem is the term.
Ecosystem/Integrations is the page (in place of Ecosystem/LongChain
Ecosystem).
As a result, a user gets a single place to start with the individual
integration.
this makes it so we dont throw errors when importing langchain when
sqlalchemy==1.3.1
we dont really want to support 1.3.1 (seems like unneccessary maintance
cost) BUT we would like it to not terribly error should someone decide
to run on it
# Add human message as input variable to chat agent prompt creation
This PR adds human message and system message input to
`CHAT_ZERO_SHOT_REACT_DESCRIPTION` agent, similar to [conversational
chat
agent](7bcf238a1a/langchain/agents/conversational_chat/base.py (L64-L71)).
I met this issue trying to use `create_prompt` function when using the
[BabyAGI agent with tools
notebook](https://python.langchain.com/en/latest/use_cases/autonomous_agents/baby_agi_with_agent.html),
since BabyAGI uses “task” instead of “input” input variable. For normal
zero shot react agent this is fine because I can manually change the
suffix to “{input}/n/n{agent_scratchpad}” just like the notebook, but I
cannot do this with conversational chat agent, therefore blocking me to
use BabyAGI with chat zero shot agent.
I tested this in my own project
[Chrome-GPT](https://github.com/richardyc/Chrome-GPT) and this fix
worked.
## Request for review
Agents / Tools / Toolkits
- @vowelparrot
# Fix bilibili api import error
bilibili-api package is depracated and there is no sync module.
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Fixes#2673#2724
## Before submitting
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# TextLoader auto detect encoding and enhanced exception handling
- Add an option to enable encoding detection on `TextLoader`.
- The detection is done using `chardet`
- The loading is done by trying all detected encodings by order of
confidence or raise an exception otherwise.
### New Dependencies:
- `chardet`
Fixes#4479
## Before submitting
<!-- If you're adding a new integration, include an integration test and
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- @eyurtsev
---------
Co-authored-by: blob42 <spike@w530>
# Load specific file types from Google Drive (issue #4878)
Add the possibility to define what file types you want to load from
Google Drive.
```
loader = GoogleDriveLoader(
folder_id="1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5",
file_types=["document", "pdf"]
recursive=False
)
```
Fixes ##4878
## Who can review?
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maintainers/contributors who might be interested:
DataLoaders
- @eyurtsev
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: UmerHA <40663591+UmerHA@users.noreply.github.com>
#docs: text splitters improvements
Changes are only in the Jupyter notebooks.
- added links to the source packages and a short description of these
packages
- removed " Text Splitters" suffixes from the TOC elements (they made
the list of the text splitters messy)
- moved text splitters, based on the length function into a separate
list. They can be mixed with any classes from the "Text Splitters", so
it is a different classification.
## Who can review?
@hwchase17 - project lead
@eyurtsev
@vowelparrot
NOTE: please, check out the results of the `Python code` text splitter
example (text_splitters/examples/python.ipynb). It looks suboptimal.
# Added another helpful way for developers who want to set OpenAI API
Key dynamically
Previous methods like exporting environment variables are good for
project-wide settings.
But many use cases need to assign API keys dynamically, recently.
```python
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="OPENAI_API_KEY")
```
## Before submitting
```bash
export OPENAI_API_KEY="..."
```
Or,
```python
import os
os.environ["OPENAI_API_KEY"] = "..."
```
<hr>
Thank you.
Cheers,
Bongsang
# Documentation for Azure OpenAI embeddings model
- OPENAI_API_VERSION environment variable is needed for the endpoint
- The constructor does not work with model, it works with deployment.
I fixed it in the notebook.
(This is my first contribution)
## Who can review?
@hwchase17
@agola
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Add bs4 html parser
* Some minor refactors
* Extract the bs4 html parsing code from the bs html loader
* Move some tests from integration tests to unit tests
# Add generic document loader
* This PR adds a generic document loader which can assemble a loader
from a blob loader and a parser
* Adds a registry for parsers
* Populate registry with a default mimetype based parser
## Expected changes
- Parsing involves loading content via IO so can be sped up via:
* Threading in sync
* Async
- The actual parsing logic may be computatinoally involved: may need to
figure out to add multi-processing support
- May want to add suffix based parser since suffixes are easier to
specify in comparison to mime types
## Before submitting
No notebooks yet, we first need to get a few of the basic parsers up
(prior to advertising the interface)
It's currently not possible to change the `TEMPLATE_TOOL_RESPONSE`
prompt for ConversationalChatAgent, this PR changes that.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Update deployments doc with langcorn API server
API server example
```python
from fastapi import FastAPI
from langcorn import create_service
app: FastAPI = create_service(
"examples.ex1:chain",
"examples.ex2:chain",
"examples.ex3:chain",
"examples.ex4:sequential_chain",
"examples.ex5:conversation",
"examples.ex6:conversation_with_summary",
)
```
More examples: https://github.com/msoedov/langcorn/tree/main/examples
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Docs and code review fixes for Docugami DataLoader
1. I noticed a couple of hyperlinks that are not loading in the
langchain docs (I guess need explicit anchor tags). Added those.
2. In code review @eyurtsev had a
[suggestion](https://github.com/hwchase17/langchain/pull/4727#discussion_r1194069347)
to allow string paths. Turns out just updating the type works (I tested
locally with string paths).
# Pre-submission checks
I ran `make lint` and `make tests` successfully.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
# Fix Homepage Typo
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested... not sure
# Docs: improvements in the `retrievers/examples/` notebooks
Its primary purpose is to make the Jupyter notebook examples
**consistent** and more suitable for first-time viewers.
- add links to the integration source (if applicable) with a short
description of this source;
- removed `_retriever` suffix from the file names (where it existed) for
consistency;
- removed ` retriever` from the notebook title (where it existed) for
consistency;
- added code to install necessary Python package(s);
- added code to set up the necessary API Key.
- very small fixes in notebooks from other folders (for consistency):
- docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb
- docs/modules/indexes/vectorstores/examples/pinecone.ipynb
- docs/modules/models/llms/integrations/cohere.ipynb
- fixed misspelling in langchain/retrievers/time_weighted_retriever.py
comment (sorry, about this change in a .py file )
## Who can review
@dev2049
# Remove unused variables in Milvus vectorstore
This PR simply removes a variable unused in Milvus. The variable looks
like a copy-paste from other functions in Milvus but it is really
unnecessary.
# Fix TypeError in Vectorstore Redis class methods
This change resolves a TypeError that was raised when invoking the
`from_texts_return_keys` method from the `from_texts` method in the
`Redis` class. The error was due to the `cls` argument being passed
explicitly, which led to it being provided twice since it's also
implicitly passed in class methods. No relevant tests were added as the
issue appeared to be better suited for linters to catch proactively.
Changes:
- Removed `cls=cls` from the call to `from_texts_return_keys` in the
`from_texts` method.
Related to:
https://github.com/hwchase17/langchain/pull/4653
# Remove unnecessary comment
Remove unnecessary comment accidentally included in #4800
## Before submitting
- no test
- no document
## Who can review?
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# Fixed typos (issues #4818 & #4668 & more typos)
- At some places, it said `model = ChatOpenAI(model='gpt-3.5-turbo')`
but should be `model = ChatOpenAI(model_name='gpt-3.5-turbo')`
- Fixes some other typos
Fixes#4818, #4668
## Who can review?
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maintainers/contributors who might be interested:
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
Previously, the client expected a strict 'prompt' or 'messages' format
and wouldn't permit running a chat model or llm on prompts or messages
(respectively).
Since many datasets may want to specify custom key: string , relax this
requirement.
Also, add support for running a chat model on raw prompts and LLM on
chat messages through their respective fallbacks.
# Your PR Title (What it does)
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# Fix subclassing OpenAIEmbeddings
Fixes#4498
## Before submitting
- Problem: Due to annotated type `Tuple[()]`.
- Fix: Change the annotated type to "Iterable[str]". Even though
tiktoken use
[Collection[str]](095924e02c/tiktoken/core.py (L80))
type annotation, but pydantic doesn't support Collection type, and
[Iterable](https://docs.pydantic.dev/latest/usage/types/#typing-iterables)
is the closest to Collection.
# fix: agenerate miss run_manager args in llm.py
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Fixes # (issue)
fix: agenerate miss run_manager args in llm.py
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